Cholera epidemic in Guinea-Bissau (2008): the importance of "place"

Abstract

Background: As resources are limited when responding to cholera outbreaks, knowledge about where to orient interventions is crucial. We describe the cholera epidemic affecting Guinea-Bissau in 2008 focusing on the geographical spread in order to guide prevention and control activities.

Methodology/principal findings: We conducted two studies: 1) a descriptive analysis of the cholera epidemic in Guinea-Bissau focusing on its geographical spread (country level and within the capital); and 2) a cross-sectional study to measure the prevalence of houses with at least one cholera case in the most affected neighbourhood of the capital (Bairro Bandim) to detect clustering of households with cases (cluster analysis). All cholera cases attending the cholera treatment centres in Guinea-Bissau who fulfilled a modified World Health Organization clinical case definition during the epidemic were included in the descriptive study. For the cluster analysis, a sample of houses was selected from a satellite photo (Google Earth™); 140 houses (and the four closest houses) were assessed from the 2,202 identified structures. We applied K-functions and Kernel smoothing to detect clustering. We confirmed the clustering using Kulldorff's spatial scan statistic. A total of 14,222 cases and 225 deaths were reported in the country (AR = 0.94%, CFR = 1.64%). The more affected regions were Biombo, Bijagos and Bissau (the capital). Bairro Bandim was the most affected neighborhood of the capital (AR = 4.0). We found at least one case in 22.7% of the houses (95%CI: 19.5-26.2) in this neighborhood. The cluster analysis identified two areas within Bairro Bandim at highest risk: a market and an intersection where runoff accumulates waste (p<0.001).

Conclusions/significance: Our analysis allowed for the identification of the most affected regions in Guinea-Bissau during the 2008 cholera outbreak, and the most affected areas within the capital. This information was essential for making decisions on where to reinforce treatment and to guide control and prevention activities.

Figure 1. Weekly number of cholera cases and case fatality ratio (CFR%) in Guinea-Bissau 2008–2009.

Figure 2. Geographical distribution of the crude cholera attack rate by region and sub-regions in Guinea- Bissau, 2008–2009.

Coordinates expressed in sexagesimal degrees.

Figure 3. Age and gender adjusted cholera attack rates (%) by Sanitary Area in Sector Autónomo de Bissau, 2008–2009.

Coordinates expressed in sexagesimal degrees. * Sanitary area with a cholera treatment centre. + Sanitary area with a cholera treatment unit.

Figure 4. Differences of K-functions and 95% confidence intervals between households with cholera cases and households without cases in Bairro Bandim (Bissau), 2008–2009.

A homogeneous set of points in the plane is a set that is distributed such that approximately the same number of points occurs in any circular region of a given area. A set of points that lacks homogeneity is spatially clustered. The k-function is defined as the expected number of points within a distance s of an arbitrary point, divided by the overall density of the points. Due to variations in the spatial distribution of the population at risk, a k-function computed only for cases may not be informative. Instead, the k-function calculated for cases can be compared with the one calculated for non-cases, with the difference between the two functions representing a measure of the extra-aggregation of cases over and above the observed for the non-cases. This difference is represented in the figure above, showing extra-aggregation of cases.

Figure 5. Geographical distribution of the probability of finding a house with at least one cholera case in Bairro Bandim (Bissau) in percentage and areas over the 95% confidence interval.

Coordinates expressed in sexagesimal degrees. Figure 5a shows the Google Earth™ picture and the overimposed image of the risk surface (probability of finding a house with at least one cholera case). The figure 5b shows the risk surface and the two areas with statistically significant higher risk (black bold line). The same two clusters were detected (dashed blue circles) using the Kulldorff's spatial scan statistic (cluster A: Log likelihood ratio = 9.95, P = 0.029; cluster B: Log likelihood ratio = 8.81, P = 0.05).